The network is trained using multi-speaker classification. As optimization criterion we employ the Additive Angular Margin (AAM) loss, which has shown notable improvements over the plain Cross Entropy loss [1]}. Moreover, the \(l_2\) -norm that applies to both embeddings and speaker representations of the classification head (i.e. the final linear layer before the Softmax) makes cosine-similarity the natural choice for measuring the similarity between enrolment and test utterances. We set the scale coefficient of the loss equal to 30, while for the margin coefficient we adopt a curriculum learning approach and progressively increase it as [0.1,0.2,0.3] during training [2]}.
